In various application domains one often encounters multivariate time series data, such as stock market prices, road traffic flows, or weather conditions in different cities, to name a few [1]}. Predicting new trends and patterns in time series domains based on historical observations is the focus of many research efforts. In particular, in the last decade, data-driven based models have shown promising results in weather forecasting applications.
